Sharing pharmaceutical research Increased collaboration will enhance our ability to predict new therapeutic drug candidates. Such data sharing is currently limited by concerns about intellectual property and competing commercial interests. Hie et al. introduce an end-to-end pipeline, using modern cryptographic tools, for secure pharmacological collaboration. Multiple entities can thus securely combine their private datasets to collectively obtain more accurate predictions of new drug-target interactions. The computational pipeline is practical, producing results with improved accuracy in a few days over a wide area network on a real dataset with more than a million interactions. Science, this issue p. 347 A computational protocol enables private pharmacological data to be securely combined. Although combining data from multiple entities could power life-saving breakthroughs, open sharing of pharmacological data is generally not viable because of data privacy and intellectual property concerns. To this end, we leverage modern cryptographic tools to introduce a computational protocol for securely training a predictive model of drug–target interactions (DTIs) on a pooled dataset that overcomes barriers to data sharing by provably ensuring the confidentiality of all underlying drugs, targets, and observed interactions. Our protocol runs within days on a real dataset of more than 1 million interactions and is more accurate than state-of-the-art DTI prediction methods. Using our protocol, we discover previously unidentified DTIs that we experimentally validated via targeted assays. Our work lays a foundation for more effective and cooperative biomedical research.

中文翻译：

---

实现私人和实用的药理学合作

共享药物研究 加强合作将增强我们预测新治疗药物候选者的能力。这种数据共享目前受到对知识产权和竞争性商业利益的担忧的限制。希等人。使用现代加密工具引入端到端管道，以实现安全的药理学协作。因此，多个实体可以安全地组合他们的私人数据集，共同获得对新药物-靶标相互作用的更准确预测。计算管道是实用的，在具有超过一百万次交互的真实数据集上，在几天内在广域网中产生精度更高的结果。科学，这个问题 p。347 计算协议使私人药理学数据能够安全地组合。虽然结合来自多个实体的数据可以推动挽救生命的突破，但由于数据隐私和知识产权问题，药理数据的开放共享通常是不可行的。为此，我们利用现代加密工具引入了一种计算协议，用于在汇集数据集上安全地训练药物-靶标相互作用 (DTI) 的预测模型，该模型通过可证明地确保所有潜在药物、靶标、并观察到相互作用。我们的协议在超过 100 万次交互的真实数据集上运行几天，并且比最先进的 DTI 预测方法更准确。使用我们的协议，我们发现了以前未识别的 DTI，我们通过靶向检测进行了实验验证。